Image registration

Relate is a correlative software package optimised to work with EM, EDS, EBSD, & AFM data and images.  It provides the tools you need to correlate data from different microscopes, visualise multi-layered data in 2D and 3D, and conduct correlative analyses.

• Combining data from different imaging modalities (e.g. AFM, EDS & EBSD)

• Interactive display of multi-layer correlated data

• Analytical tools for metadata interrogation

• Documented workflows and processes

Correlate

• Import data from AZtec using the H5oina file format
• Import AFM data
• Correlate both sets of data using intuitive image overlays and image matching tools
• Produce combined multimodal datasets

Visualise

• 2D display of multi-layered data
• 3D visualisation of topography combined with AFM material properties, EM images, and EDS & EBSD map overlays
• Customisation of colour palettes, data overlays, image rendering options, and document display
• Export images and animations

Analyse

• Generate profile (cross section) views of multimodal data
• Measure and quantify data across multiple layers
• Analyse areas via data thresholding using amount of x-ray counts, phase maps, height, or other material properties.
• Select an extensive range of measurement parameters
• Export analytical data to text or CSV files

Correlia is an open-source ImageJ/FIJI plug-in for the registration of 2D multi-modal microscopy data-sets. The software is developed at ProVIS - Centre for Correlative Microscopy and is specifically designed for the needs of chemical microscopy involving various micrographs as well as chemical maps at different resolutions and field-of-views.

ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

MIA is designed for “out-of-the-box” compatibility with spatially-calibrated 5D images, yielding measurements in both pixel and physical units.  Functionality can be extended both internally, via integration with SciJava’s scripting interface, and externally, with Java modules that extend the MIA framework. Both have full access to all objects and images in the analysis workspace.

Workflows are, by default, compatible with batch processing multiple files within a single folder. Thanks to Bio-Formats, MIA has native support for multi-series image formats such as Leica .lif and Nikon .nd2.

Workflows can be automated from initial image loading through processing, object detection, measurement extraction, visualisation, and data exporting. MIA includes near 200 modules integrated with key ImageJ plugins such as Bio-Formats, TrackMate and Weka Trainable Segmentation.

Module(s) can be turned on/off dynamically in response to factors such as availability of images and objects, user inputs and measurement-based filters. Switches can also be added to “processing view” for easy workflow control.

MIA is developed in the Wolfson Bioimaging Facility at the University of Bristol.

ClearMap is a toolbox for the analysis and registration of volumetric data from cleared tissues.

It was initially developed to map brain activity at cellular resolution in whole mouse brains using immediate early gene expression. It has since then been extended as a tool for the qunatification of whole mouse brain vascualtur networks at capilary resolution.

It is composed of sevral specialized modules or scripts: tubemap, cellmap, WobblyStitcher.

ClearMap has been designed to analyze O(TB) 3d datasets obtained via light sheet microscopy from iDISCO+ cleared tissue samples immunolabeled for proteins. The ClearMap tools may also be useful for data obtained with other types of microscopes, types of markers, clearing techniques, as well as other species, organs, or samples.

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default. Images need to be prepared such that they contain only one channel, but channel of ineterst (to be transformed with the same transformation) can be given as input, and Transform Virtual Stack plugin can be used as well.